A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification mo...

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Bibliographic Details
Main Authors: Marek Herde, Denis Huseljic, Bernhard Sick, Adrian Calma
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Active learning
classification
error-prone annotators
human-in-the-loop learning
interactive learning
machine learning
Online Access:https://ieeexplore.ieee.org/document/9650877/

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